Upload 6 files

Dockerfile

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+FROM python:3.9
+
+COPY . .
+
+WORKDIR /
+
+RUN pip install --no-cache-dir -r ./requirements.txt
+
+CMD ["uvicorn","main:app","--host","0.0.0.0","--port","7860"]

app.py

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+import streamlit as st
+import requests
+import json
+from io import BytesIO
+
+st.title('Face Expression Prediction')
+
+image_file = st.file_uploader("Upload Image")
+
+if image_file is not None:
+    image = image_file.getvalue()
+
+    response = response.post(
+        "https://dpatel9923",
+        files = {
+            "image": ByteIO(image)
+        }
+    )
+
+    label = json.loads(response._content)
+    st.write(f"Expression of Face is {label['label']}")

main.py

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+from fastapi import FastAPI, UploadFile, file_uploader
+import json
+from PIL import Image
+from io import BytesIO
+import numpy as np
+from model import build_model
+app = FastAPI()
+
+#Load model
+image_shape = (224,224,3)
+num_classes = 6
+model = build_model(image_shape, num_classes)
+model.load_weights('./model_with_weights.h5')
+classes = {
+    0: 'Ahegao',
+    1: 'Angry',
+    2: 'Happy',
+    3: 'Neutral'.
+    4: 'Sad',
+    5: 'Surprise'
+}
+
+@app.get("/")
+def first_api():
+    return {
+        "response": "Face Expression Prediction"
+    }
+
+@app.post("/prediction")
+async def prediction(image: UploadFile = File(...)):
+    image = await image.read()
+
+    # process image
+    image = Image.open(BytesIO(image))
+    image = image.resize((image_shape[0], image_shape[1]))
+    image = image.convert('L')
+    image = np.expand_dims(image, axis=2)
+
+    image = np.expand_dims(image, axis=0)
+    prediction = model.predict(image)[0]
+    label = np.argmax(prediction, axis=-1).tolist()
+
+    return {
+        "label": label,
+        "class": classes[label]
+    }

requirements.txt

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+fastapi
+uvicorn[standard]
+streamlit
+tensorflow==2.15.0